Meta's Open Networking for AI at Scale

Meta is sharing next-generation open networking innovations at OCP Summit 2026 to power AI infrastructure at scale.

Editorial position: Meta's OCP Summit 2026 announcements focus on open networking innovations that address thermal efficiency, latency, and scalability for AI workloads.

The constraint behind the work

Large-scale AI training demands infrastructure that can handle massive parallelism with minimal latency and maximum thermal efficiency. Traditional air-cooled data centers struggle to meet these requirements as GPU clusters grow into gigawatt-scale installations. Meta's approach targets three primary constraints:

• Thermal density: Keeping accelerator clusters cool as they approach 5 gigawatts.
• Interconnect latency: Ensuring low-latency communication across petabit-scale networks.
• Fabric scalability: Building resilient, flexible fabrics that can span buildings, campuses, and regions.

The engineering choice

Meta elected to develop and open-source a suite of networking technologies rather than relying solely on proprietary solutions. This includes:

• IcePack: A liquid-cooled network rack designed to manage thermal density at scale.
• IceCube: Switch blades offering 102.4T bandwidth and 128×800G non-blocking capacity.
• Flattened Planar Fabric (FPF): A scale-out fabric architecture for building large, low-latency GPU clusters.
• Open Switch Platform Architecture (OSPA): A collaborative effort with Microsoft and Alibaba to standardize the switch platform layer.

In practice: These choices reflect a move toward vendor-agnostic, standards-based hardware that can be deployed across diverse infrastructure footprints.

How it works

IcePack and IceCube

IcePack integrates liquid cooling directly into the network rack, coupling it with IceCube switch blades. Each IceCube blade provides 102.4T of bandwidth and supports 128×800G non-blocking switching capacity. This combination allows Meta to maintain low latency while managing the heat output of dense AI accelerator clusters.

Flattened Planar Fabric (FPF)

FPF is a scale-out fabric design that reduces complexity and latency in large GPU clusters. It enables Meta to build highly efficient, resilient networks without the traditional multi-layer switching overhead.

Backend Aggregation (BAG) Layer

The BAG layer interconnects AI clusters across buildings, campuses, and regions using petabit-scale capacity. It employs eBGP link-bandwidth signaling for traffic distribution and damping to prevent routing churn. Training remains resilient through automatic replica exclusion and synchronization across the BAG.

Segment Routing over IPv6 (SRv6)

Meta deploys SRv6 for regional backbone networks, providing a flexible, policy-driven routing framework that simplifies network management and enhances resilience.

Tahan and Next-Generation Scale-Up Blade

Tahan is an Ethernet-based scale-up switch with 64x800G total capacity. Meta also previewed a next-generation scale-up blade featuring 72 accelerators per rack, further increasing processing density.

MetaRoCE

MetaRoCE is Meta's open-source RDMA transport protocol for AI cluster networks over Ethernet, improving performance and reducing latency for inter-accelerator communication.

The scale assumptions

These innovations are designed for deployments at the following scales:

• Prometheus: Expected to operate at roughly 1 gigawatt in 2026.
• Hyperion: Scaling toward 5 gigawatts by 2026.
• BAG Layer: Supporting petabit-scale interconnectivity across multiple regions.
• IceCube: Providing 102.4T per switch blade, with aggregate capacities measured in petabits.

Watch out: The success of these technologies hinges on the ability to manage thermal and electrical loads at gigawatt scales, which introduces unique operational challenges.

Operational lessons

1. Standardization reduces complexity: By collaborating on initiatives like OSPA and ESUN, Meta aims to simplify maintenance and troubleshooting through standardized switch platform layers and open specifications for scale-up topologies.
2. Liquid cooling is essential: Direct liquid cooling in racks like IcePack is necessary to handle the thermal density of modern AI clusters.
3. Fabric design impacts latency: Architectures like FPF demonstrate that thoughtful fabric design can significantly reduce latency in large-scale GPU clusters.
4. Resilience requires automation: Features like automatic replica exclusion and eBGP damping are critical for maintaining training resilience across large, dynamic networks.

Trade-offs the source accepts

• Omitted performance benchmarks: The source does not provide specific performance comparisons to proprietary technologies, making it difficult to quantify improvements.
• High complexity at scale: Managing gigawatt-scale deployments introduces operational complexity that is not fully detailed in the source material.
• Collaborative development: While collaboration reduces vendor lock-in, it also requires coordination across multiple organizations, potentially slowing iteration speeds.

What smaller teams can reuse

Smaller teams can adopt the following concepts even at reduced scales:

• Open specifications: Engaging with initiatives like OSPA and ESUN to gain access to standardized designs and reduce dependency on proprietary vendors.
• Liquid cooling principles: Applying liquid cooling strategies to manage thermal density in denser accelerator deployments, even if full immersion cooling is not feasible.
• Fabric simplification: Exploring simplified fabric architectures to reduce latency and complexity in cluster interconnects, regardless of scale.
• RDMA over Ethernet: Utilizing protocols like MetaRoCE to improve inter-accelerator communication efficiency.

Who should act

• Infrastructure engineers working on large-scale AI training clusters.
• Data center architects designing for high-density compute and thermal management.
• Network designers seeking to reduce latency and increase resilience in GPU cluster interconnects.
• Open-source contributors interested in collaborative efforts to standardize networking hardware and protocols.

Bottom line

Meta's OCP Summit 2026 announcements present a comprehensive set of open networking innovations aimed at addressing the thermal, latency, and scalability challenges of large-scale AI infrastructure. While specific performance metrics and implementation details remain undisclosed, the focus on standardization, liquid cooling, and resilient fabric design offers valuable lessons for the broader industry.

Sources

OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks